Used Cars Prediction Analysis¶
There are numerous aspects to take into consideration while purchasing a car – the main being should you buy a new or a used car. If you are trying to manage your finances wisely, opting for a pre-owned car would be a wise decision. Though the idea of purchasing a new car may sound tempting, the quick rate of depreciation, higher price, and greater insurance, among others, do not work in the favor of new cars.
Importing necessary libraries¶
Data Preprocessing¶
| Unnamed: 0 | Name | Location | Year | Kilometers_Driven | Fuel_Type | Transmission | Owner_Type | Mileage | Engine | Power | Seats | New_Price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Maruti Alto K10 LXI CNG | Delhi | 2014 | 40929 | CNG | Manual | First | 32.26 km/kg | 998 CC | 58.2 bhp | 4.0 | NaN |
| 1 | 1 | Maruti Alto 800 2016-2019 LXI | Coimbatore | 2013 | 54493 | Petrol | Manual | Second | 24.7 kmpl | 796 CC | 47.3 bhp | 5.0 | NaN |
| 2 | 2 | Toyota Innova Crysta Touring Sport 2.4 MT | Mumbai | 2017 | 34000 | Diesel | Manual | First | 13.68 kmpl | 2393 CC | 147.8 bhp | 7.0 | 25.27 Lakh |
| 3 | 3 | Toyota Etios Liva GD | Hyderabad | 2012 | 139000 | Diesel | Manual | First | 23.59 kmpl | 1364 CC | null bhp | 5.0 | NaN |
| 4 | 4 | Hyundai i20 Magna | Mumbai | 2014 | 29000 | Petrol | Manual | First | 18.5 kmpl | 1197 CC | 82.85 bhp | 5.0 | NaN |
| Unnamed: 0 | Name | Location | Year | Kilometers_Driven | Fuel_Type | Transmission | Owner_Type | Mileage | Engine | Power | Seats | New_Price | Price | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Maruti Wagon R LXI CNG | Mumbai | 2010 | 72000 | CNG | Manual | First | 26.6 km/kg | 998 CC | 58.16 bhp | 5.0 | NaN | 1.75 |
| 1 | 1 | Hyundai Creta 1.6 CRDi SX Option | Pune | 2015 | 41000 | Diesel | Manual | First | 19.67 kmpl | 1582 CC | 126.2 bhp | 5.0 | NaN | 12.50 |
| 2 | 2 | Honda Jazz V | Chennai | 2011 | 46000 | Petrol | Manual | First | 18.2 kmpl | 1199 CC | 88.7 bhp | 5.0 | 8.61 Lakh | 4.50 |
| 3 | 3 | Maruti Ertiga VDI | Chennai | 2012 | 87000 | Diesel | Manual | First | 20.77 kmpl | 1248 CC | 88.76 bhp | 7.0 | NaN | 6.00 |
| 4 | 4 | Audi A4 New 2.0 TDI Multitronic | Coimbatore | 2013 | 40670 | Diesel | Automatic | Second | 15.2 kmpl | 1968 CC | 140.8 bhp | 5.0 | NaN | 17.74 |
Unnamed: 0 int64 Name object Location object Year int64 Kilometers_Driven int64 Fuel_Type object Transmission object Owner_Type object Mileage object Engine object Power object Seats float64 New_Price object Price float64 dtype: object
Unnamed: 0 int64 Name object Location object Year int64 Kilometers_Driven int64 Fuel_Type object Transmission object Owner_Type object Mileage object Engine object Power object Seats float64 New_Price object dtype: object
Rows in dataset are : 6019 Columns in dataset are : 14
Rows in test dataset are : 1234 Columns in test dataset are : 13
| Unnamed: 0 | Year | Kilometers_Driven | Seats | Price | |
|---|---|---|---|---|---|
| count | 6019.000000 | 6019.000000 | 6.019000e+03 | 5977.000000 | 6019.000000 |
| mean | 3009.000000 | 2013.358199 | 5.873838e+04 | 5.278735 | 9.479468 |
| std | 1737.679967 | 3.269742 | 9.126884e+04 | 0.808840 | 11.187917 |
| min | 0.000000 | 1998.000000 | 1.710000e+02 | 0.000000 | 0.440000 |
| 25% | 1504.500000 | 2011.000000 | 3.400000e+04 | 5.000000 | 3.500000 |
| 50% | 3009.000000 | 2014.000000 | 5.300000e+04 | 5.000000 | 5.640000 |
| 75% | 4513.500000 | 2016.000000 | 7.300000e+04 | 5.000000 | 9.950000 |
| max | 6018.000000 | 2019.000000 | 6.500000e+06 | 10.000000 | 160.000000 |
| Unnamed: 0 | Year | Kilometers_Driven | Seats | |
|---|---|---|---|---|
| count | 1234.000000 | 1234.000000 | 1234.000000 | 1223.000000 |
| mean | 616.500000 | 2013.400324 | 58507.288493 | 5.284546 |
| std | 356.369424 | 3.179700 | 35598.702098 | 0.825622 |
| min | 0.000000 | 1996.000000 | 1000.000000 | 2.000000 |
| 25% | 308.250000 | 2011.000000 | 34000.000000 | 5.000000 |
| 50% | 616.500000 | 2014.000000 | 54572.500000 | 5.000000 |
| 75% | 924.750000 | 2016.000000 | 75000.000000 | 5.000000 |
| max | 1233.000000 | 2019.000000 | 350000.000000 | 10.000000 |
Unnamed: 0 0 Name 0 Location 0 Year 0 Kilometers_Driven 0 Fuel_Type 0 Transmission 0 Owner_Type 0 Mileage 2 Engine 36 Power 36 Seats 42 New_Price 5195 Price 0 dtype: int64
Missing values in first list: {'Maruti Ritz VDi ABS', 'Fiat Punto EVO 1.3 Emotion', 'Honda BR-V i-DTEC S MT', 'Honda Amaze VX CVT i-VTEC', 'Hyundai Verna 1.4 CX', 'Land Rover Discovery 4 TDV6 Auto Diesel', 'Toyota Etios Liva 1.4 VXD', 'Chevrolet Spark 1.0 PS', 'Maruti Ignis 1.2 AMT Delta', 'Maruti SX4 ZXI AT', 'Tata Tiago AMT 1.2 Revotron XTA', 'Fiat Punto 1.4 Emotion', 'Mahindra KUV 100 mFALCON D75 K6 5str AW', 'Toyota Innova 2.5 LE 2014 Diesel 8 Seater', 'Hyundai Creta 1.6 VTVT Base', 'Chevrolet Enjoy 1.3 TCDi LTZ 7', 'Chevrolet Sail Hatchback 1.2', 'Jeep Compass 1.4 Sport', 'Maruti Swift AMT ZXI', 'Honda BRV i-DTEC V MT', 'Hyundai Creta 1.6 SX Automatic', 'Hindustan Motors Contessa 2.0 DSL', 'Maruti Wagon R VXI AMT Opt', 'Maruti Swift VVT ZXI', 'Nissan Teana XL', 'Mahindra KUV 100 G80 K4 Plus 5Str', 'Ford Endeavour 3.0L AT 4x2', 'Hyundai Creta 1.6 SX Diesel', 'Fiat Linea Classic 1.3 Multijet', 'Mahindra Xylo E9', 'Volkswagen CrossPolo 1.2 TDI', 'Tata Indica Vista Aqua 1.2 Safire', 'Maruti A-Star Zxi', 'Hyundai Sonata Embera 2.4L MT', 'Maruti Ertiga VXI Petrol', 'Mahindra Xylo H9', 'Chevrolet Enjoy Petrol LTZ 7 Seater', 'Hyundai i20 new Sportz AT 1.4', 'Mahindra KUV 100 mFALCON D75 K2', 'Maruti Celerio X VXI Option', 'Mahindra Bolero SLX', 'Hyundai Elantra SX AT', 'Ford Fiesta Classic 1.6 SXI Duratec', 'Mercedes-Benz CLA 45 AMG', 'BMW X3 2.5si', 'Hyundai Accent GLX', 'Skoda Octavia 2.0 TDI MT Style', 'Mercedes-Benz E-Class E240 V6 AT', 'Mahindra KUV 100 mFALCON G80 K4 5str', 'Hyundai Elantra GT', 'Toyota Etios Cross 1.2L G', 'Mahindra Scorpio VLS 2.2 mHawk', 'Fiat Grande Punto 1.2 Emotion', 'Honda BR-V i-VTEC VX MT', 'Toyota Innova 2.0 V', 'Hyundai Tucson 2.0 e-VGT 4WD AT GLS', 'Nissan 370Z AT', 'Hyundai i20 Active SX Diesel', 'Skoda Superb Petrol Ambition', 'Isuzu MU 7 4x2 HIPACK', 'Skoda Rapid Ultima 1.6 TDI Ambition Plus', 'Skoda Laura 1.8 TSI Ambition', 'Honda Jazz 2020 Petrol', 'Ford Classic 1.4 Duratorq CLXI', 'Land Rover Freelander 2 S Business Edition', 'Maruti Ciaz VXi', 'Hyundai Xcent 1.2 CRDi SX', 'Toyota Innova 2.5 GX 8 STR', 'Ford Freestyle Titanium Plus Diesel', 'Chevrolet Enjoy TCDi LS 7 Seater', 'Fiat Avventura FIRE Dynamic', 'Honda Amaze E i-DTEC', 'Hyundai Santro Xing XG AT eRLX Euro III', 'Hyundai i20 1.4 Asta AT (O) with Sunroof', 'Fiat Avventura Urban Cross 1.3 Multijet Emotion', 'BMW 7 Series 740i Sedan', 'Tata Indica Vista Quadrajet LX', 'Volkswagen Polo ALLSTAR 1.2 MPI', 'Bentley Flying Spur W12', 'Skoda Laura L and K MT', 'Mahindra Scorpio S10 8 Seater', 'Tata Sumo EX 10/7 Str BSII', 'Hyundai Verna Transform SX VGT CRDi BS III', 'Nissan Micra XL CVT', 'Honda Civic 2010-2013 1.8 V AT', 'Fiat Abarth 595 Competizione', 'Mahindra Thar 4X4', 'Maruti 800 DX', 'Maruti Vitara Brezza ZDi Plus AMT', 'Mahindra TUV 300 2015-2019 T8 AMT', 'Honda Jazz VX CVT', 'Mahindra Scorpio VLX Special Edition BS-IV', 'Mahindra Bolero Power Plus ZLX', 'Maruti Versa DX2', 'Toyota Camry MT with Moonroof', 'Mercedes-Benz B Class B180 Sports', 'Hyundai i20 2015-2017 1.4 CRDi Sportz', 'Jaguar XF 2.0 Petrol Portfolio', 'Volvo S60 D5 Kinetic', 'Tata Indica V2 DiCOR DLG BS-III', 'Hyundai Santro LS zipDrive Euro I', 'Honda City i DTec VX Option BL', 'Renault Koleos 4X2 MT', 'OpelCorsa 1.4Gsi', 'Audi Q5 2008-2012 3.0 TDI Quattro', 'Tata Manza Club Class Safire90 LX', 'Mercedes-Benz A Class Edition 1', 'Mahindra Scorpio SLX 2.6 Turbo 8 Str', 'Toyota Corolla Altis GL', 'Toyota Etios Liva VD', 'Land Rover Discovery 4 SDV6 SE', 'Tata Indica Vista Terra Quadrajet 1.3L BS IV', 'Honda CR-V Diesel', 'Maruti Alto XCITE', 'Nissan Terrano XE 85 PS', 'Volkswagen Vento 1.5 TDI Highline Plus', 'Tata Tiago 1.05 Revotorq XT Option', 'Mahindra Verito Vibe 1.5 dCi D6', 'BMW 3 Series GT 320d Sport Line', 'Honda Mobilio V i VTEC', 'BMW 7 Series 730Ld DPE Signature', 'Hyundai Elite i20 Magna Plus', 'Honda Accord 2001-2003 2.3 VTI L MT', 'Volkswagen Vento 1.2 TSI Comfortline AT', 'Honda City ZX VTEC Plus', 'Toyota Etios Liva Diesel TRD Sportivo', 'Maruti Ciaz VDi Option SHVS', 'Hyundai Santro Xing GLS CNG', 'Datsun GO T Petrol', 'Mitsubishi Pajero Sport 4X2 AT', 'Maruti Vitara Brezza ZDi AMT', 'Tata Indica Vista Aqua TDI BSIII', 'Ford Ikon 1.4 ZXi', 'Hyundai i20 2015-2017 Magna Optional 1.4 CRDi', 'Hyundai EON 1.0 Kappa Magna Plus', 'Hyundai EON 1.0 Era Plus', 'Chevrolet Enjoy 1.4 LTZ 8', 'Hyundai Accent Executive LPG', 'Mahindra Scorpio VLX 2WD BSIII', 'Skoda Laura 1.9 TDI MT Elegance', 'Hyundai Verna Transform VTVT with Audio', 'Mahindra TUV 300 P4', 'Toyota Innova Crysta Touring Sport 2.4 MT', 'Ford Fiesta Classic 1.6 Duratec LXI', 'Renault Pulse RxZ', 'BMW 5 Series 520d Sedan', 'Land Rover Range Rover HSE', 'Maruti Swift 1.3 VXi', 'Volkswagen Vento 1.6 Trendline', 'Ford Fiesta 1.4 SXI Duratorq', 'Renault Duster 85PS Diesel RxZ', 'Mercedes-Benz E-Class 250 D W 124', 'Mercedes-Benz S Class 2005 2013 320 L', 'BMW 5 Series 530i Sport Line', 'Volkswagen Vento 1.5 TDI Highline Plus AT', 'Fiat Linea Dynamic', 'Mercedes-Benz GLA Class 220 d 4MATIC', 'Mahindra KUV 100 D75 K8 5Str', 'Audi Q3 30 TDI S Edition', 'Honda WRV i-DTEC VX', 'Ford EcoSport 1.5 Petrol Ambiente', 'Tata Indica Vista Terra 1.2 Safire BS IV', 'Volkswagen Jetta 2007-2011 1.6 Trendline', 'Tata Tigor 1.2 Revotron XZ Option', 'Mercedes-Benz B Class B180 Sport', 'Renault Lodgy 110PS RxL', 'Honda Civic 2010-2013 1.8 S MT Inspire', 'Toyota Land Cruiser Prado VX L'}
False
Missing values in first list: {'Bentley Flying', 'Toyota Land', 'OpelCorsa 1.4Gsi', 'Nissan 370Z', 'Fiat Abarth', 'Hindustan Motors', 'Isuzu MU'}
Missing values in first list: set()
Unnamed: 0 0 Name 0 Location 0 Year 0 Kilometers_Driven 0 Fuel_Type 0 Transmission 0 Owner_Type 0 Mileage 0 Engine 0 Power 0 Seats 0 Price 0 Cars 0 dtype: int64
Unnamed: 0 0 Name 0 Location 0 Year 0 Kilometers_Driven 0 Fuel_Type 0 Transmission 0 Owner_Type 0 Mileage 0 Engine 0 Power 0 Seats 0 Price 0 Cars 0 dtype: int64
Name 0 Location 0 Year 0 Kilometers_Driven 0 Fuel_Type 0 Transmission 0 Owner_Type 0 Mileage 0 Engine 0 Power 0 Seats 0 Cars 0 dtype: int64
Name object Location object Year int64 Kilometers_Driven int64 Fuel_Type object Transmission object Owner_Type object Mileage float64 Engine float64 Power float64 Seats float64 Cars object dtype: object
Data Analysis¶
Price 1.000000 Power 0.769351 Engine 0.659117 Year 0.305800 Seats 0.052262 Kilometers_Driven -0.011263 Mileage -0.313877 Name: Price, dtype: float64
Conclusion- According to the stats for choosing a necessary and optimum used car, a customer will prefer price at the first place and mileage at the last accordingly. An average typical second hand car customer prefers decent price at its first place.
[<matplotlib.lines.Line2D at 0x1fa7f74ed90>]
Conclusion- Converted the value of Price to Log(Price) for a good solution to have a more normal visualization of the distribution of the Price.
Conclusion- The above pie chart indicates the price of particular fuel engines(diesel, petrol, CNG, LPG) Also it indicates that the market price of diesel engines is more as compared to other fuel type engines. Also diesel users are greater in market compared to others as it gives better mileage.
Conclusion- According to the plot, the customers using automatic transmission mode vehicles are increasing rapidly in consecutive years.
Conclusion- According to the plot, the customers using diesel driven vehicles are increasing rapidly in consecutive years.
Conclusion- From graph it is clear that in CNG and LPG driven cars only manual mode of transmission is available whereas automatic mode of transmission leads in diesel and petrol driven cars(disesel being the most used).
Conclusion- The graph clearly indicates that people prefer Manual mode of Transmission over Automatic one
array(['Mumbai', 'Pune', 'Chennai', 'Coimbatore', 'Hyderabad', 'Jaipur',
'Kochi', 'Kolkata', 'Delhi', 'Bangalore', 'Ahmedabad'],
dtype=object)
array(['Maruti Wagon', 'Hyundai Creta', 'Honda Jazz', 'Maruti Ertiga',
'Audi A4', 'Hyundai EON', 'Nissan Micra', 'Toyota Innova',
'Volkswagen Vento', 'Tata Indica', 'Maruti Ciaz', 'Honda City',
'Maruti Swift', 'Land Rover', 'Mitsubishi Pajero', 'Honda Amaze',
'Renault Duster', 'Mercedes-Benz New', 'BMW 3', 'Maruti S',
'Audi A6', 'Hyundai i20', 'Maruti Alto', 'Honda WRV',
'Toyota Corolla', 'Mahindra Ssangyong', 'Maruti Vitara',
'Mahindra KUV', 'Mercedes-Benz M-Class', 'Volkswagen Polo',
'Tata Nano', 'Hyundai Elantra', 'Hyundai Xcent', 'Mahindra Thar',
'Hyundai Grand', 'Renault KWID', 'Hyundai i10', 'Nissan X-Trail',
'Maruti Zen', 'Ford Figo', 'Mercedes-Benz C-Class',
'Porsche Cayenne', 'Mahindra XUV500', 'Nissan Terrano',
'Honda Brio', 'Ford Fiesta', 'Hyundai Santro', 'Tata Zest',
'Maruti Ritz', 'BMW 5', 'Toyota Fortuner', 'Ford Ecosport',
'Hyundai Verna', 'Datsun GO', 'Maruti Omni', 'Toyota Etios',
'Jaguar XF', 'Maruti Eeco', 'Honda Civic', 'Volvo V40',
'Mercedes-Benz B', 'Mahindra Scorpio', 'Honda CR-V',
'Mercedes-Benz SLC', 'BMW 1', 'Chevrolet Beat', 'Skoda Rapid',
'Audi RS5', 'Mercedes-Benz S', 'Skoda Superb', 'BMW X5',
'Mercedes-Benz GLC', 'Mini Countryman', 'Chevrolet Optra',
'Renault Lodgy', 'Mercedes-Benz E-Class', 'Maruti Baleno',
'Skoda Laura', 'Mahindra NuvoSport', 'Skoda Fabia', 'Tata Indigo',
'Audi Q3', 'Skoda Octavia', 'Audi A8', 'Mahindra Verito',
'Mini Cooper', 'Hyundai Santa', 'BMW X1', 'Hyundai Accent',
'Hyundai Tucson', 'Mercedes-Benz GLE', 'Maruti A-Star',
'Fiat Grande', 'BMW X3', 'Ford EcoSport', 'Audi Q7',
'Volkswagen Jetta', 'Mercedes-Benz GLA', 'Maruti Celerio',
'Tata Sumo', 'Honda Accord', 'BMW 6', 'Tata Manza',
'Chevrolet Spark', 'Mini Clubman', 'Nissan Teana', 'Maruti 800',
'Honda BRV', 'Jaguar XE', 'Tata Xenon', 'Audi A3',
'Mercedes-Benz GL-Class', 'Honda BR-V', 'Volvo S80',
'Renault Captur', 'Chevrolet Enjoy', 'Mahindra Bolero', 'Audi Q5',
'Mitsubishi Cedia', 'Maruti S-Cross', 'Skoda Yeti',
'Ford Endeavour', 'Mercedes-Benz GLS', 'Mercedes-Benz A',
'Maruti SX4', 'Toyota Camry', 'Honda Mobilio', 'Fiat Linea',
'Audi TT', 'Mahindra Renault', 'Jeep Compass', 'Ford Ikon',
'Chevrolet Sail', 'Mahindra Quanto', 'Chevrolet Aveo',
'Mahindra Xylo', 'Maruti Esteem', 'Tata Safari', 'Maruti Ignis',
'Jaguar XJ', 'Nissan Sunny', 'Mercedes-Benz SLK-Class',
'Volkswagen Passat', 'Maruti Dzire', 'Chevrolet Cruze',
'Renault Koleos', 'Toyota Qualis', 'Volkswagen Ameo',
'Maruti Grand', 'Datsun redi-GO', 'Smart Fortwo',
'Mitsubishi Outlander', 'Porsche Cayman', 'Mercedes-Benz CLA',
'Volvo XC60', 'Tata New', 'Porsche Boxster', 'Mahindra XUV300',
'Tata Hexa', 'Tata Tiago', 'BMW 7', 'Fiat Avventura', 'Tata Tigor',
'Volvo S60', 'Ambassador Classic', 'Volkswagen Beetle',
'Fiat Petra', 'Hyundai Getz', 'Audi A7', 'Hyundai Elite',
'Ford Aspire', 'Volkswagen Tiguan', 'Chevrolet Captiva',
'Fiat Punto', 'Mahindra TUV', 'BMW X6', 'Tata Bolt',
'Nissan Evalia', 'Renault Scala', 'Mahindra Jeep',
'Hyundai Sonata', 'Ford Freestyle', 'Mahindra Logan',
'Chevrolet Tavera', 'Volvo XC90', 'Renault Pulse',
'Mitsubishi Montero', 'Porsche Panamera', 'Volkswagen CrossPolo',
'Renault Fluence', 'Tata Venture', 'Tata Nexon', 'Isuzu MUX',
'Toyota Platinum', 'Mercedes-Benz R-Class',
'Mercedes-Benz CLS-Class', 'ISUZU D-MAX', 'Mercedes-Benz S-Class',
'Mitsubishi Lancer', 'Ford Classic', 'Datsun Redi', 'Ford Mustang',
'Ford Fusion', 'Fiat Siena', 'Maruti 1000',
'Mercedes-Benz SL-Class', 'BMW Z4', 'Force One', 'Maruti Versa',
'Honda WR-V', 'Bentley Continental', 'Lamborghini Gallardo',
'Jaguar F'], dtype=object)
Location Ahmedabad 223 Bangalore 353 Chennai 490 Coimbatore 634 Delhi 549 Hyderabad 741 Jaipur 410 Kochi 648 Kolkata 530 Mumbai 784 Pune 613 Name: Cars, dtype: int64
Observation- From above data, we can observe that Mumbai and Hyderabad has maximum number of second hand car users which is our target audience.
[<matplotlib.lines.Line2D at 0x1fa00e6c6a0>]
Location Ahmedabad Volvo XC60 Bangalore Volvo V40 Chennai Volvo S80 Coimbatore Volvo S60 Delhi Volvo S60 Hyderabad Volvo XC60 Jaipur Volkswagen Vento Kochi Volvo XC90 Kolkata Volkswagen Vento Mumbai Volvo S60 Pune Volvo XC60 Name: Cars, dtype: object
Conclusion- The above data provides information that the respective corresponding car models is of the highest demand in that particular city. From this result we can conclude that since Hyderabad and Mumbai have the highest number of second hand car users thus the availability of their respective cars ie. Volvo XC60 and Volvo S60 respectively are the target cars with maximum number of selling units. Similarly, the selling units of the corresponding cars is more in their respective locations. Hence it can be the our main selling main point.
Conclusion - This is a powerbi report comparing various columns of the data provided giving clear analysis of relation among themselves.
Model fitting¶
| model | Root Mean Squared Error | Accuracy on Traing set | Accuracy on Testing set | |
|---|---|---|---|---|
| 3 | MLPRegressor | 211.896605 | 0.675261 | 0.626082 |
| 4 | AdaBoostRegressor | 150.480611 | 0.82538 | 0.811423 |
| 0 | DecisionTreeRegressor | 115.714253 | 0.999993 | 0.888493 |
| 2 | RandomForestRegressor | 83.299006 | 0.991954 | 0.942216 |
| 5 | ExtraTreesRegressor | 80.05228 | 0.999993 | 0.946633 |
| 1 | XGBRegressor | 74.815814 | 0.994635 | 0.953386 |
| Car_id | Price | |
|---|---|---|
| 0 | 0 | 163.59 |
| 1 | 1 | 111.39 |
| 2 | 2 | 944.99 |
| 3 | 3 | 170.60 |
| 4 | 4 | 286.35 |
Observation- The above model displays prediction of the car price for respective specifications given in the feature1 array.
Prediction by User Input¶
Enter your own data to test the model:
There was an error when executing cell [54]. Please run Voilà with --show_tracebacks=True or --debug to see the error message, or configure VoilaConfiguration.show_tracebacks.